Using Viral Dynamics to Connect Clinical Markers of Disease Progression to Sequence Evolution for HIV-1

HIV-1 remains a global health challenge, with over 35 million people infected. The high rates of turnover and evolutionary adaptability exhibited by HIV-1 pose a particular challenge to the use of antiretrovirals, as well as the development of a vaccine. Our focus is to understand the dynamics of two of the most commonly tracked clinical markers of an HIV-1 infection: CD4+ T cells/mm3 (CD4 count) and HIV-1 RNA/ml (viral load). We are developing a dynamic mathematical model of HIV-1 infection that uses equilibration, adaptation, and inheritance to model the initial infection by a founding virus as well as successive generations of viral lineages. We have calibrated our model to match viral load set points and rates of CD4 decline from 91 HIV-infected individuals studied longitudinally during early stages of the disease. We plan to incorporate sustained tissue damage and antiretroviral treatment to study how drug resistance could develop.

Andrew Adams
Faculty Supervisor: 
Dr. Alexander (Sandy) Rutherford
Project Year: 
British Columbia